Deep Hierarchical Parsing for Semantic Segmentation

This paper proposes a learning-based approach to scene parsing inspired by the deep Recursive Context Propagation Network (RCPN). RCPN is a deep feed-forward neural network that utilizes the contextual information from the entire image, through bottom-up followed by top-down context propagation via random binary parse trees. This improves the feature representation of every super-pixel in the image for better classification into semantic categories. We analyze RCPN and propose two novel contributions to further improve the model. We first analyze the learning of RCPN parameters and discover the presence of bypass error paths in the computation graph of RCPN that can hinder contextual propagation. We propose to tackle this problem by including the classification loss of the internal nodes of the random parse trees in the original RCPN loss function. Secondly, we use an MRF on the parse tree nodes to model the hierarchical dependency present in the output. Both modifications provide performance boosts over the original RCPN and the new system achieves state-of-the-art performance on Stanford Background, SIFT-Flow and Daimler urban datasets.Comment: IEEE CVPR 201

Measurement of Low Matric Potentials with Porous Matrix Sensors and Water-Filled Tensiometers

Water-filled tensiometers are widely used to measure the matric potential of soil water. It is often assumed that, because these give a direct reading, they are accurate. With a series of laboratory tests with model laboratory systems of increasing complexity we show that the output of water-filled tensiometers can, particularly in drying soils, be in serious error. Specifically, we demonstrated that water-filled tensiometers can indicate a steady matric potential, typically between –60 and –90 kPa, when the soil is much drier. We demonstrate the use of water-filled tensiometers that can measure matric potentials smaller than –100 kPa in the laboratory and in the field. The physics of the failure of water-filled tensiometers is discussed. When the matric potential was greater than –60 kPa, in laboratory and field tests water-filled and porous matrix sensors were in good agreement. In the field environment the porous matrix sensor was useful because it allowed early detection of the failure of water-filled tensiometers. In dry soils (matric potential <–60 kPa) the porous matrix sensor was more reliable and accurate than the water-filled tensiometer

Pedestrian Recognition with a Learned Metric

This paper presents a new method for viewpoint invariant pedestrian recognition problem. We use a metric learning framework to obtain a robust metric for large margin nearest neighbor classification with rejection (i.e., classifier will return no matches if all neighbors are beyond a certain distance). The rejection condition necessitates the use of a uniform threshold for a. maximum allowed distance for deeming a, pair of images a match. In order to handle the rejection case, we propose a novel cost similar to the Large Margin Nearest Neighbor (LMNN) method and call our approach Large Margin Nearest Neighbor with Rejection (LMNN-R). Our method is able to achieve significant improvement over previously reported results on the standard Viewpoint Invariant Pedestrian Recognition (VIPeR [1]) dataset

End-point energy measurements of field emission current in a continuous-wave normal-conducting rf injector

The LANL/AES normal-conducting radio-frequency injector has been tested at cw cathode gradients up to 10  MV/m. Field-emission electrons from a roughened copper cathode are accelerated to beam energy as high as 2.5 MeV and impinge on a stainless steel target. The energies of the resulting bremsstrahlung photons are measured at varying levels of injector cavity rf power corresponding to different accelerating gradients. At low cavity power, the bremsstrahlung spectra exhibit well-defined end-point energies at the positions where the number of single-photon events decreases to one (S/N ratio=1). Increasing the cavity power raises the probability of two-photon events in which two photons simultaneously arrive at the detector and register counts at twice the photon energy. The end-point energies at high cavity power are recorded at positions where the single-photon events transition to two-photon events. The measured end-point energies using this method are in excellent agreement with PARMELA calculations based on the cavity gradients deduced from the cavity rf power measurements